Known-class Aware Self-ensemble for Open Set Domain Adaptation

Pytorch implemention of our method for open set domain adaptation. Based on this implementation, our result is ranked 2nd in the VisDA Challenge 2018.

Enviorment

The code is developed under the following configuration.

Hardware:

1 GPU (with at least 11G GPU memories), which is set for the correspoinding batch size.

Software:

Python 3, Pytorch 0.4, and CUDA 8.0 are necessary before running the scripts. To install the required pythonn packages (expect Pytorch), run

pip install -r requirements.txt

Datasets

Follow the github REPO to download the Syn2Real-O dataset, and put it in the ./data/visda folder.

Training Examples

Source only

python train_source_only.py --config cfgs/source_only_exp001.yaml

Adabn

python train_adabn.py --config cfgs/adabn_exp001.yaml

MMD

python train_mmd.py --config cfgs/mmd_exp001.yaml

BP

python train_bp.py --config cfgs/bp_exp001.yaml

Self-Ensemble

python train_se.py --config cfgs/se_exp001.yaml

KASE

python train_kase.py --config cfgs/kase_exp001.yaml

Run KASE on Office A -> D dataset

python train_kase_office.py --config cfgs/office/kase_a_d_exp001.yaml

Test the model

python test_multi.py --config your_yaml

License

This project is licensed under the MIT License - see the LICENSE.md file for details